CN110705680B - Subarray position optimization method and system of distributed array - Google Patents
Subarray position optimization method and system of distributed array Download PDFInfo
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Abstract
The invention discloses a subarray position optimization method and a subarray position optimization system of a distributed array, which belong to the technical field of distributed array antennas and comprise the following steps: s1: setting optimization parameters and initializing populations; s2: performing cross operation; s3: carrying out mutation operation; s4: and finishing the optimization process. According to the invention, the similarity detection work is carried out on the two individuals to be crossed, so that the similarity of the two individuals can be effectively judged in time, the possibility of crossing of similar or identical individuals is effectively reduced, and the optimization effect can be improved; and according to the individual fitness condition in the population, the mutation probability is dynamically and adaptively adjusted, so that the position optimization result of the sub-array in the distributed array can be effectively improved, the sidelobe level of the distributed array antenna is better inhibited, the service performance of the distributed antenna is improved, and the distributed array antenna is worthy of being popularized and used.
Description
Technical Field
The invention relates to the technical field of distributed array antennas, in particular to a subarray position optimization method and a subarray position optimization system of a distributed array.
Background
In the information age, human beings widely use electromagnetic waves to transmit and exchange information. The radar works by emitting electromagnetic waves to a detection target and then correspondingly processing echoes reflected by the target to acquire required information such as parameters of the irradiation target. The radar itself is a modern electronic device with strong adaptability and long-distance detection capability, and has been widely developed and applied in the military and civil fields since the birth of the people due to the excellent characteristics of the radar. With the continuous development and progress of society, people are continuously pursuing higher resolution and larger space radiation power to meet the requirements of more accurate target positioning and longer detection distance.
On the premise that the array elements and the number of the sub-arrays are the same, compared with an equidistant array, the non-equidistant array has the same array elements, but the fuzzy influence is reduced, so that the array system has higher resolution, and can obtain smaller errors when the array system carries out DOA estimation. Therefore, many scholars have conducted extensive research on non-equidistant arrays. The safety administration commander of the twentieth research institute of China electronics science and technology group company firstly establishes a topological structure of the distributed array antenna, then provides an improved genetic algorithm on the basis of the traditional genetic algorithm, particularly adds a disturbance strategy into the algorithm, improves the convergence of the algorithm, and finally optimizes the position of the subarray-level distributed array by using the improved genetic algorithm. The method is characterized in that an array comprehensive algorithm combining an off-grid technology and a particle swarm algorithm is provided by the university of electronic technology, the particle swarm algorithm is firstly used for primary position optimization, and then the off-grid technology is combined to realize gradient optimization, namely secondary optimization in grid units, so that the optimal position of an array element is found, and the feasibility and the accuracy of the algorithm are verified through simulation experiments. The method comprises the steps of firstly using a distributed array formed by two linear arrays as a model, deducing a mathematical expression of an array directional diagram of the distributed array, then analyzing and researching beam width and resolution through simulation, then adopting a genetic algorithm, and on the premise that the width of a main lobe of the distributed array is in a certain range, using the position of a sub-array and the amplitude of an array element as optimization variables and minimizing a side lobe level as an optimization target to realize the suppression of the grating lobe of the distributed array antenna. To date, there has been little research on subarray-level distributed arrays. Therefore, a subarray position optimization method and a subarray position optimization system of the distributed array are provided.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: how to effectively improve the optimization effect of the position of the sub-array in the distributed array, and the method for optimizing the position of the sub-array in the distributed array is provided, the method can effectively judge the similarity of two individuals in time, thereby effectively reducing the possibility of crossing similar or identical individuals and improving the optimization effect; and the mutation probability is dynamically and adaptively adjusted, so that the position optimization result of the sub-array in the distributed array is effectively improved, the sidelobe level of the distributed array antenna is better inhibited, and the service performance of the distributed antenna is improved.
The invention solves the technical problems through the following technical scheme, and the invention comprises the following steps:
s1: setting optimization parameters and initializing population
Setting optimization parameters, and randomly generating position information of a plurality of sub-arrays as an initial population;
s2: performing a crossover operation
Calculating the fitness value of each individual in the population, randomly selecting two individuals in the population to carry out similarity detection work, and judging whether direct cross operation can be carried out or not by a similarity judgment formula which is as follows:
|AD(Xi)-AD(Xj)|≤γ
wherein, XiAnd XjRespectively representing two individuals to be crossed, AD (X)i) And AD (X)j) Respectively representing the adaptation of two individuals to be crossedThe strain value, gamma, represents the similarity detection threshold value;
if two individuals satisfy the discrimination formula, then the individual X is determinediAnd XjIf the similarity is higher, the replacement operation is performed first, and then the cross operation is performed;
otherwise, judging the individual XiAnd XjThe method has lower similarity, directly carries out the cross operation, and can effectively judge the similarity of two individuals in time by carrying out the similarity detection work on the two individuals to be crossed, thereby effectively reducing the possibility of crossing similar or identical individuals and improving the optimization effect;
s3: performing mutation operation
And performing mutation operation on the two individuals after the cross operation is finished, wherein the value of the mutation probability VR in the mutation operation is calculated according to the following relational expression:
wherein, ADmaxIs the value with the highest individual fitness value in the contemporary population, ADavgThe method has the advantages that the dynamic self-adaptive adjustment is carried out on the variation probability for the average value of the fitness values of all individuals in the iterative population according to the fitness condition of the individuals in the population, so that the optimization result of the position of the sub-array in the distributed array can be effectively improved, the sidelobe level of the distributed array antenna is better inhibited, the use performance of the distributed array antenna is improved, and the method is worthy of being popularized and used;
s4: end of optimization procedure
After the mutation operation is finished, judging whether a termination condition is met, if so, finishing the optimization process, and outputting a position information optimization result of each subarray in the distributed array;
otherwise, continuously and randomly selecting two individuals in the population, repeating the steps S2-S4 until the termination condition is met, ending the optimization process, and outputting the position information optimization result of each sub-array in the distributed array.
Further, in the step S1, the optimization parameter is the mutation probability, and the parameter directly affects the searching ability of the algorithm.
Further, in step S1, the process of randomly generating the position information of the plurality of sub-arrays is to randomly generate a set of numbers representing the position information of the plurality of sub-arrays.
Further, in step S2, the fitness value of each individual in the population is calculated by a fitness function.
Further, the fitness function is a peak sidelobe level of the distributed array pattern.
Further, in step S2, the crossover operation is to exchange partial chromosomes of two individuals, wherein the partial chromosome of one individual becomes the chromosome of the other individual.
Further, in the step S2, the replacing operation is to replace the individual with the low fitness value of the two individuals with the newly generated individual.
Further, in step S3, the mutation operation is to change chromosomes in two individuals, and randomly generate new location information to replace the previous location information.
Further, in step S4, the termination condition is that the peak side lobe level requirement is satisfied or the upper limit of the iteration number of the algorithm is reached.
The invention also provides a subarray position optimization system of the distributed array, which comprises:
the initialization module is used for setting optimization parameters and randomly generating position information of a plurality of sub-arrays as an initial population;
the cross operation module is used for calculating the fitness value of each individual in the population, randomly selecting two individuals in the population to carry out similarity detection work, judging whether the direct cross operation can be carried out or not through a similarity discrimination formula and executing the cross operation;
the mutation operation module is used for performing mutation operation on the two individuals after the cross operation is finished;
a termination judgment module used for judging whether a termination condition is met or not after the mutation operation is finished, if so, ending the optimization process, otherwise, continuously and randomly selecting two individuals in the population, and repeating the steps S2-S4 until the termination condition is met;
the central processing module is used for sending instructions to other modules to complete related actions;
the initialization module, the cross operation module, the mutation operation module and the termination judgment module are all electrically connected with the central processing module.
Compared with the prior art, the invention has the following advantages: according to the subarray position optimization method and system of the distributed array, the similarity of two individuals can be effectively judged in time by carrying out similarity detection work on the two individuals to be crossed, so that the possibility of crossing of similar or identical individuals is effectively reduced, and the optimization effect can be improved; and according to the individual fitness condition in the population, the mutation probability is dynamically and adaptively adjusted, so that the position optimization result of the sub-array in the distributed array can be effectively improved, the sidelobe level of the distributed array antenna is better inhibited, the service performance of the distributed antenna is improved, and the distributed array antenna is worthy of being popularized and used.
Drawings
FIG. 1 is a schematic overall flow chart of an optimization method according to a first embodiment of the present invention;
FIG. 2 is a schematic flow chart of an implementation of the optimization method according to a second embodiment of the present invention;
FIG. 3 is a comparison graph of peak side lobe level variation curves in the third embodiment of the present invention.
Detailed Description
The following examples are given for the detailed implementation and specific operation of the present invention, but the scope of the present invention is not limited to the following examples.
Example one
As shown in fig. 1, the present embodiment provides a technical solution: a subarray position optimization method of a distributed array comprises the following steps:
s1: setting optimization parameters and initializing population
Setting optimization parameters, and randomly generating position information of a plurality of sub-arrays as an initial population;
s2: performing a crossover operation
Calculating the fitness value of each individual in the population, randomly selecting two individuals in the population to carry out similarity detection work, and judging whether direct cross operation can be carried out or not by a similarity judgment formula which is as follows:
|AD(Xi)-AD(Xj)|≤γ
wherein, XiAnd XjRespectively representing two individuals to be crossed, AD (X)i) And AD (X)j) Respectively representing the fitness values of two individuals to be crossed, wherein gamma represents a similarity detection threshold value;
if two individuals satisfy the discrimination formula, then the individual X is determinediAnd XjIf the similarity is higher, the replacement operation is performed first, and then the cross operation is performed;
otherwise, judging the individual XiAnd XjThe method has lower similarity, directly carries out the cross operation, and can effectively judge the similarity of two individuals in time by carrying out the similarity detection work on the two individuals to be crossed, thereby effectively reducing the possibility of crossing similar or identical individuals and improving the optimization effect;
s3: performing mutation operation
And performing mutation operation on the two individuals after the cross operation is finished, wherein the value of the mutation probability VR in the mutation operation is calculated according to the following relational expression:
wherein, ADmaxIs the value with the highest individual fitness value in the contemporary population, ADavgThe method is an average value of fitness values of all individuals in an iterative population, and performs dynamic self-adaptive adjustment on the variation probability according to the fitness condition of the individuals in the population, so that the neutron adaptability of the distributed array can be effectively improvedThe array position optimization result enables the sidelobe level of the distributed array antenna to be better inhibited, improves the service performance of the distributed antenna and is worth being popularized and used;
s4: end of optimization procedure
After the mutation operation is finished, judging whether a termination condition is met, if so, finishing the optimization process, and outputting a position information optimization result of each subarray in the distributed array;
otherwise, continuously and randomly selecting two individuals in the population, repeating the steps S2-S4 until the termination condition is met, ending the optimization process, and outputting the position information optimization result of each sub-array in the distributed array.
In step S1, the optimization parameter is the mutation probability, and the parameter directly affects the searching ability of the algorithm.
In step S1, the process of randomly generating the position information of the plurality of sub-arrays is to randomly generate a group of numbers representing the position information of the plurality of sub-arrays.
In step S2, fitness values of the individuals in the population are calculated by a fitness function.
The fitness function is a peak sidelobe level of the distributed array pattern.
In said step S2, the crossover operation is to swap the partial chromosomes of the two individuals, wherein the partial chromosome of one individual becomes the chromosome of the other individual.
In step S2, the replacing operation is to replace the individual with a low fitness value of the two individuals with the newly generated individual.
In step S3, the mutation operation is to change chromosomes in two individuals, and randomly generate new location information to replace the previous location information.
In step S4, the termination condition is that the peak side lobe level requirement is satisfied or the upper limit of the iteration number of the algorithm is reached.
The present embodiment further provides a subarray position optimization system of a distributed array, including:
the initialization module is used for setting optimization parameters and randomly generating position information of a plurality of sub-arrays as an initial population;
the cross operation module is used for calculating the fitness value of each individual in the population, randomly selecting two individuals in the population to carry out similarity detection work, judging whether the direct cross operation can be carried out or not through a similarity discrimination formula and executing the cross operation;
the mutation operation module is used for performing mutation operation on the two individuals after the cross operation is finished;
a termination judgment module used for judging whether a termination condition is met or not after the mutation operation is finished, if so, ending the optimization process, otherwise, continuously and randomly selecting two individuals in the population, and repeating the steps S2-S4 until the termination condition is met;
the central processing module is used for sending instructions to other modules to complete related actions;
the initialization module, the cross operation module, the mutation operation module and the termination judgment module are all electrically connected with the central processing module.
Example two
As shown in fig. 2, the distributed array position optimization algorithm based on the improved genetic algorithm provided by the present embodiment includes the following steps:
step one, in the cross operation of an improved algorithm, similarity detection is carried out on two individuals;
and step two, in the mutation operation of the improved algorithm, the value of the mutation probability VR is adaptively changed.
In step one, the specific content of similarity detection for two individuals in the crossover operation of the improved algorithm is as follows:
before randomly selecting two different individuals to carry out cross operation, firstly detecting the similarity of the two individuals, and judging whether the next cross operation is 'close reproduction'. If the similarity degree of the two individuals to be crossed is higher, the two individuals to be crossed are regarded as 'close-relative propagation', and then the individuals with low fitness value in the two individuals are replaced by the newly generated individuals, and then the cross operation is carried out, namely, part of chromosome exchanging operation is carried out; if the similarity degree of the two individuals to be crossed is lower, the two individuals are directly subjected to cross operation, namely part of chromosome exchange operation, so that a new individual is generated.
Therefore, a corresponding improvement method is provided for the problem that the cross operation is easy to occur, and the specific steps are as follows:
s1: after randomly selecting two paired individuals to be crossed, firstly performing similarity detection on the individuals to be crossed, and judging whether chromosome exchange can be directly performed or not by detecting the similarity of the individuals, as shown in formula (1):
|AD(Xi)-AD(Xj)|≤γ (1)
wherein, XiAnd XjRespectively representing two individuals to be crossed, AD (X)i) And AD (X)j) Respectively representing the fitness values of two individuals to be crossed, and gamma represents a similarity detection threshold value.
If the two individuals satisfy formula (1), the individual X is determinediAnd XjIf the similarity is higher, the subsequent replacement operation is performed, and S2 is executed;
if formula (1) is not satisfied, the individual X is determinediAnd XjWith lower similarity, the two individuals can directly perform chromosome swapping.
S2: and carrying out individual replacement, namely selecting an individual with a smaller fitness value from the two individuals, replacing the individual with a new individual generated randomly to carry out cross operation with the individual with a larger fitness value, wherein the step can effectively reduce the possibility of cross operation of similar or same individuals.
In step two, in the mutation operation of the improved algorithm, the specific content of adaptively changing the value of the mutation probability VR is as follows:
the basic idea of improving the variation operation in the algorithm is to adopt a self-adaptive idea and dynamically adjust the value of the variation probability VR according to the fitness condition of individuals in a population, so as to obtain a better optimization effect.
Since VR ∈ [0,1], VR can be expressed by the following formula (2):
wherein, ADmaxIs the value with the highest individual fitness value in the contemporary population, ADavgThe average value of the fitness values of all individuals in the iterative population is used, so that the variation probability VR can be dynamically adjusted according to the fitness values of the population.
EXAMPLE III
To evaluate the performance of the present invention, the following simulation experiments were performed in this example.
As shown in fig. 3, for the distributed array antenna, a conventional genetic algorithm and an improved genetic algorithm are respectively used for optimization, so as to obtain a peak side lobe level variation curve, where the total aperture L of the distributed array is 100 λ, the number M of sub-arrays is 5, the number N of array elements of a single sub-array is 10, the distance d between adjacent array elements in the sub-array is λ/2, and the wavelength λ is 1. Population size NP equal to 100, maximum number of iterations GmaxThe crossover probability CR is 0.9, the mutation probability VR is 0.1, and the single-point crossover mode and the similarity detection threshold γ are 0.5. Next, the optimization method of the present invention performs an optimization solution on the subarray positions of the distributed array.
In summary, the method and the system for optimizing the position of the subarray of the distributed array in the three embodiments can effectively determine the similarity of two individuals in time by performing similarity detection on the two individuals to be intersected, thereby effectively reducing the possibility of the intersection of similar or identical individuals and improving the optimization effect; and according to the individual fitness condition in the population, the mutation probability is dynamically and adaptively adjusted, so that the position optimization result of the sub-array in the distributed array can be effectively improved, the sidelobe level of the distributed array antenna is better inhibited, the service performance of the distributed antenna is improved, and the distributed array antenna is worthy of being popularized and used.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "a plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made to the above embodiments by those of ordinary skill in the art within the scope of the present invention.
Claims (8)
1. A subarray position optimization method of a distributed array is characterized by comprising the following steps:
s1: setting optimization parameters and initializing population
Setting optimization parameters, and randomly generating position information of a plurality of subarrays as an initial population;
s2: performing a crossover operation
Calculating the fitness value of each individual in the population, randomly selecting two individuals in the population to carry out similarity detection work, and judging whether direct cross operation can be carried out or not by a similarity judgment formula which is as follows:
|AD(Xi)-AD(Xj)|≤γ
wherein, XiAnd XjRespectively representing two individuals to be crossed, AD (X)i) And AD (X)j) Respectively representing the fitness values of two individuals to be crossed, wherein gamma represents a similarity detection threshold value;
if two individuals satisfy the discrimination formula, then the individual X is determinediAnd XjIf the similarity is higher, the replacement operation is performed first, and then the cross operation is performed;
otherwise, judging the individual XiAnd XjThe method has lower similarity and directly carries out cross operation;
in step S2, the fitness value of each individual in the population is calculated by a fitness function, where the fitness function is a peak side lobe level of the distributed array pattern;
s3: performing mutation operation
And performing mutation operation on the two individuals after the cross operation is finished, wherein the value of the mutation probability VR in the mutation operation is calculated according to the following relational expression:
wherein, ADmaxIs the value with the highest individual fitness value in the contemporary population, ADavgThe average value of fitness values of all individuals in the iterative population is obtained;
s4: end of optimization procedure
After the mutation operation is finished, judging whether a termination condition is met, if so, finishing the optimization process, and outputting a position information optimization result of each subarray in the distributed array;
otherwise, continuously and randomly selecting two individuals in the population, repeating the steps S2-S4 until the termination condition is met, ending the optimization process, and outputting the position information optimization result of each sub-array in the distributed array.
2. The method of optimizing subarray positions of a distributed array of claim 1, wherein: in step S1, the optimization parameter is the mutation probability.
3. The method of optimizing subarray positions of a distributed array of claim 1, wherein: in step S1, the process of randomly generating the position information of the plurality of sub-arrays is to randomly generate a group of numbers representing the position information of the plurality of sub-arrays.
4. The method of optimizing subarray positions of a distributed array of claim 1, wherein: in said step S2, the crossover operation is to swap the partial chromosomes of the two individuals, wherein the partial chromosome of one individual becomes the chromosome of the other individual.
5. The method of optimizing subarray positions of a distributed array of claim 1, wherein: in step S2, the replacing operation is to replace the individual with a low fitness value of the two individuals with the newly generated individual.
6. The method of optimizing subarray positions of a distributed array of claim 1, wherein: in step S3, the mutation operation is to change chromosomes in two individuals, and randomly generate new location information to replace the previous location information.
7. The method of optimizing subarray positions of a distributed array of claim 1, wherein: in step S4, the termination condition is that the peak side lobe level requirement is satisfied or the upper limit of the iteration number of the algorithm is reached.
8. A subarray position optimization system of a distributed array, which uses the subarray position optimization method according to any one of claims 1 to 7 to complete the subarray position optimization work of the distributed array, and is characterized by comprising:
the initialization module is used for setting optimization parameters and randomly generating position information of a plurality of sub-arrays as an initial population;
the cross operation module is used for calculating the fitness value of each individual in the population, randomly selecting two individuals in the population to carry out similarity detection work, judging whether the direct cross operation can be carried out or not through a similarity discrimination formula and executing the cross operation;
the mutation operation module is used for performing mutation operation on the two individuals after the cross operation is finished;
a termination judgment module used for judging whether a termination condition is met or not after the mutation operation is finished, if so, ending the optimization process, otherwise, continuously and randomly selecting two individuals in the population, and repeating the steps S2-S4 until the termination condition is met;
the central processing module is used for sending instructions to other modules to complete related actions;
the initialization module, the cross operation module, the mutation operation module and the termination judgment module are all electrically connected with the central processing module.
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